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A barrier function method for minimax problems

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Abstract

This paper presents an algorithm based on barrier functions for solving semi-infinite minimax problems which arise in an engineering design setting. The algorithm bears a resemblance to some of the current interior penalty function methods used to solve constrained minimization problems. Global convergence is proven, and numerical results are reported which show that the algorithm is exceptionally robust, and that its performance is comparable, while its structure is simpler than that of current first-order minimax algorithms.

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References

  1. L. Armijo, “Minimization of functions having continuous partial derivatives,”Pacific Journal of Mathematics 16 (1966) 1–3.

    Google Scholar 

  2. C. Berge,Topological Spaces (Macmillan, New York, 1963).

    Google Scholar 

  3. F.H. Clarke,Optimization and Nonsmooth Analysis (Wiley, New York, 1983).

    Google Scholar 

  4. C. Charalambous and A.R. Conn, “An efficient method to solve the minimax problem directly,”SIAM Journal of Numerical Analysis 15 (1978) 162–187.

    Google Scholar 

  5. A.R. Conn and N.I.M. Gould, “An exact penalty function for semi-infinite programming,”Mathematical Programming 37 (1987) 19–40.

    Google Scholar 

  6. I.D. Coope and G.A. Watson, “A projected Lagrangian algorithm for semi-infinite programming,”Mathematical Programming 32 (1985) 337–356.

    Google Scholar 

  7. A.V. Fiacco and G.P. McCormick, “The Sequential unconstrained minimization technique without parameters,”Operations Research 15 (1967) 820–227.

    Google Scholar 

  8. D. Goldfarb, S. Mehrotra, “Relaxed variants of Karmarkar's algorithm for linear programs with unknown objective value,”Mathematical Programming 40 (1988) 183–195.

    Google Scholar 

  9. C.C. Gonzaga, “An algorithm for solving linear programming problems in o(n 3 L) operations,” Memo No. UCB/ERL M87/10, Electronics Research Laboratory, University of California at Berkeley (Berkeley, CA, 1987).

    Google Scholar 

  10. C. Gonzaga, E. Polak and R. Trahan, “An improved algorithm for optimization problems with functional inequality constraints,”IEEE Transactions on Automatic Control AC-25 (1980) 49–54.

    Google Scholar 

  11. J. Hald and K. Madsen, “Combined LP and quasi-Newton methods for minimax optimization,”Mathematical Programming 20 (1981) 49–62.

    Google Scholar 

  12. S.P. Han, “Variable metric methods for minimizing a class of nondifferentiable functions,”Mathematical Programming 20 (1981) 1–13.

    Google Scholar 

  13. R. Hettich and W. Van Honstede, “On quadratically convergent methods for semi-infinite programming,” in: R. Hettich, ed.,Semi-Infinite Programming, Lecture Notes in Control and Information Science No. 15 (Springer, New York, 1979) pp. 97–111.

    Google Scholar 

  14. F. Jarre, “Convergence of the method of analytic centers for generalized convex programs,” Report No. 67, Schwerpunktprogramm der Deutschen Forschungsgemeinschaft—Anwendungsbezogene Optimierung und Steurerung, Institut fur Angewandte Mathematik und Statistik, Universitat Wurzburg (Wurzburg, 1988).

    Google Scholar 

  15. F. Jarre, “An implementation of the method of analytic centers,”8th Conference on Analysis and Optimization of Systems (INRIA, Antibes, France, June 1988).

    Google Scholar 

  16. K. Jittorntrum and M.R. Osborne, “Trajectory analysis and extrapolation in barrier function methods,”Australian Mathematical Society Journal Series B 20 (1978) 352–369.

    Google Scholar 

  17. N. Karmarkar, “A new polynomial-time algorithm for linear programming,”Combinatorica 4 (1984) 373–395.

    Google Scholar 

  18. R. Klessig and E. Polak, “A method of feasible directions using function approximations, with applications to min max problems,”Journal of Mathematical Analysis and Applications 41 (1973) 583–602.

    Google Scholar 

  19. K. Madsen, “An algorithm for minimax solution of overdetermined systems of non-linear equations,”Journal of the Institute of Mathematics and its Applications 16 (1975) 321–328.

    Google Scholar 

  20. D.Q. Mayne and E. Polak, “A quadratically convergent algorithm for solving infinite dimensional inequalities,”Applied Mathematics and Optimization 9 (1982) 25–40.

    Google Scholar 

  21. R. Mifflin, “Rates of convergence for a method of centers algorithm,”Journal of Optimization Theory and Applications 18 (1976) 199–228.

    Google Scholar 

  22. W. Murray and M.L. Overton, “A projected lagrangian algorithm for nonlinear minimax optimization,”SIAM Journal on Scientific and Statistical Computing 1 (1980) 201–223.

    Google Scholar 

  23. W. Oettli, “The method of feasible Directions for continuous minimax problems,” in: A. Prekopa, ed.,Survey of Mathematical Programming, Vol. 1 (North-Holland, Amsterdam, 1979).

    Google Scholar 

  24. E. Polak, “On the mathematical foundations of nondifferentiable optimization in engineering design,”SIAM Review 29 (1987) 21–89.

    Google Scholar 

  25. E. Polak, S. Salcudean and D.Q. Mayne, “Adaptive control of ARMA plants using worst case design by semi-infinite optimization,”IEEE Transactions on Automatic Control AC-32 (1987) 388–397.

    Google Scholar 

  26. E. Polak and T.S. Wuu, “On the design of stabilizing compensators via semi-infinite optimization,”IEEE Transactions on Automatic Control AC-34 (1989) 196–200.

    Google Scholar 

  27. E. Polak and D.Q. Mayne, “An algorithm for optimization problems with functional inequality constraints,”IEEE Transactions on Automatic Control AC-21 (1976) 184–193.

    Google Scholar 

  28. E. Polak and A.L. Tits, “A recursive quadratic programming algorithm for semi-infinite optimization problems,”Applied Mathematics and Optimization 8 (1982) 325–349.

    Google Scholar 

  29. B.N. Pshenichnyi and Yu.M. Danilin,Numerical Methods in Extremal Problems (Nauka, Moscow, 1975). [In Russian.]

    Google Scholar 

  30. G. Sonnevend and J. Stoer, “Global ellipsoidal approximations and homotopy methods for solving convex analytic programs,” Report No. 40, Schwerpunktprogramm der Deutschen Forschungsgemeinschaft — Anwendungsbezogene Optimierung und Steurerung, Institut fur Angewandte Mathematik und Statistik, Universitat Wurzburg (Wurzburg 1988).

    Google Scholar 

  31. G. Sonnevend, “New algorithms in convex programming based on a notion of center (for systems of analytic inequalities) and on rational extrapolation,” in: K.-H. Hoffmann et al., eds.,Trends in Mathematical Optimization, ISNM, Vol. 84 (Birkhauser, Stuttgart, 1987) pp. 311–327.

    Google Scholar 

  32. Y. Tanaka, M. Fukushima and T. Ibaraki, “A comparative study of several semi-infinite nonlinear programming algorithms,”European Journal of Operational Research 36 (1988) 92–100.

    Google Scholar 

  33. R. Tremolieres, “La method des centres a troncature variable,” PhD Thesis, University of Paris (Paris, 1968).

    Google Scholar 

  34. R.S. Womersley and R. Fletcher, “An algorithm for composite nonsmooth optimization problems,”Journal of Optimization Theory and Applications 48 (1986) 493–523.

    Google Scholar 

  35. Y. Ye, “Interior algorithms for linear, quadratic, and linearly constrained convex programming,” PhD Thesis, Department of Engineering — Economic Systems, Stanford University (Stanford, CA, 1987).

    Google Scholar 

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This research was supported by the National Science Foundation grant ECS-8517362, the Air Force Office Scientific Research grant 86-0116, the California State MICRO program, and the United Kingdom Science and Engineering Research Council.

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Polak, E., Higgins, J.E. & Mayne, D.Q. A barrier function method for minimax problems. Mathematical Programming 54, 155–176 (1992). https://doi.org/10.1007/BF01586049

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